{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcf83b17-7109-48c3-8920-2662d498210e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5128068c-2570-42b2-b9b5-8345e8c9641e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf65785-13f0-44ed-ac0d-d4d3391f5b20",
   "metadata": {},
   "source": [
    "### 设置训练集和测试集数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "283d869f-9975-4056-89e6-716b810f6ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 2 3\n",
    "target = 3\n",
    "\n",
    "if target==1:\n",
    "    data_name = '0618'\n",
    "elif target==2:\n",
    "    data_name = '0854'\n",
    "elif target==3:\n",
    "    data_name = '1066'\n",
    "\n",
    "# 设置训练集数据\n",
    "# 获取指定的CSV文件\n",
    "csv_files = glob.glob(f'./RGB_data/data_{data_name}_multi.csv')\n",
    "\n",
    "# 设置测试集数据\n",
    "new_image_path = f'./input_data/{data_name}.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d2b754-0265-4117-a546-538da2f31e9b",
   "metadata": {},
   "source": [
    "# TRAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac41cd2a-913c-4322-aa1b-f187c36f371e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(76, 4)\n"
     ]
    }
   ],
   "source": [
    "# 初始化一个空的DataFrame来存储合并后的数据\n",
    "data = pd.DataFrame()\n",
    "\n",
    "# 遍历所有CSV文件并将它们合并\n",
    "for file in csv_files:\n",
    "    df = pd.read_csv(file)\n",
    "    data = pd.concat([data, df], ignore_index=True)\n",
    "\n",
    "# 现在all_data包含了所有CSV文件的数据\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c842420-8c91-4fa3-bfa2-1f43a100dabd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((76, 3), (76,), pandas.core.frame.DataFrame, pandas.core.series.Series)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入特征和标签\n",
    "features = data.drop('Label', axis=1) # 按列操作取名为’Label以外的列\n",
    "labels = data['Label']\n",
    "features.shape,labels.shape,type(features),type(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bad1adc-8161-43ca-a09d-8f7b5cb4d764",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 归一化\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features = scaler.fit_transform(features)\n",
    "features = pd.DataFrame(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48be8177-c8cf-4f42-bbe5-72cd3071ab0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels,\n",
    "                                     test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de21f354-6867-4729-bc50-a7a23e4ffec0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 84 candidates, totalling 840 fits\n",
      "Best parameters: {'C': 100, 'class_weight': 'balanced', 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "Best cross-validation score: 0.8600000000000001\n"
     ]
    }
   ],
   "source": [
    "#网格搜索\n",
    "svc_grid = svm.SVC()\n",
    "param_grid = {\n",
    "    'C' : [100, 10, 1, 0.1, 0.01, 0.001, 0.0001],\n",
    "    'gamma' : [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "    'kernel' : ['rbf'],\n",
    "    'class_weight': [None, 'balanced']\n",
    "\n",
    "}\n",
    "#estimator是上面创建的SVC实例->svc\n",
    "grid_search = GridSearchCV(estimator=svc_grid, param_grid=param_grid,\n",
    "                           cv=10, scoring='accuracy', verbose=1)\n",
    "#模型训练\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最佳参数和最佳分数\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best cross-validation score:\", grid_search.best_score_)\n",
    "\n",
    "#使用最佳参数和训练集数据训练最终模型\n",
    "clf = grid_search.best_estimator_\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a8c410a-705f-40af-8443-9d165be3c383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共76个数据, 25个正样本，18个负样本\n",
      "\n",
      "Model accuracy: 0.84\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         5\n",
      "           1       0.75      0.86      0.80         7\n",
      "           2       0.83      0.71      0.77         7\n",
      "\n",
      "    accuracy                           0.84        19\n",
      "   macro avg       0.86      0.86      0.86        19\n",
      "weighted avg       0.85      0.84      0.84        19\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#输出训练结果\n",
    "# 数据量\n",
    "positive_count = data[data['Label'] == 1]['Label'].count()\n",
    "negative_count = data[data['Label'] == 0]['Label'].count()\n",
    "\n",
    "print(f'共{data.shape[0]}个数据, {positive_count}个正样本，{negative_count}个负样本\\n')\n",
    "\n",
    "# 计算并打印准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Model accuracy: {accuracy:.2f}\\n')\n",
    "\n",
    "# 计算并打印分类报告\n",
    "report = classification_report(y_test, y_pred)\n",
    "print('Classification Report:\\n', report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d03120b-0afc-4590-acd2-3ee25fefbac3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def plot_svm_decision_boundaries(clf, X_train, y_train):\n",
    "    # 创建一个图形窗口，大小为20x10英寸\n",
    "    fig = plt.figure(figsize=(20, 12))\n",
    "\n",
    "    # 创建四个子图，排列为2行2列\n",
    "    # axs是一个2x2的数组，每个元素代表一个子图对象\n",
    "    axs = fig.subplots(2, 2)\n",
    "\n",
    "    # 将子图数组展平，方便索引\n",
    "    axs = axs.flatten()\n",
    "\n",
    "    # BGR (3D) 图\n",
    "    # 创建3D子图，位置在左上角（221表示2行2列中的第1个）\n",
    "    ax1 = fig.add_subplot(221, projection='3d')\n",
    "    # 绘制BGR数据点\n",
    "    ax1.scatter(X_train[0], X_train[1], X_train[2], c=y_train, cmap=plt.cm.Paired)\n",
    "    # 设置坐标轴标签\n",
    "    ax1.set_xlabel('B')\n",
    "    ax1.set_ylabel('G')\n",
    "    ax1.set_zlabel('R')\n",
    "    # 设置子图标题\n",
    "    ax1.set_title('BGR Channels')\n",
    "\n",
    "    # 获取支持向量\n",
    "    support_vectors = clf.support_vectors_\n",
    "    # 在3D图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 0], support_vectors[:, 1], support_vectors[:, 2], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')\n",
    "\n",
    "    # 计算并绘制决策边界\n",
    "    w = clf.coef_[0]\n",
    "    b = clf.intercept_[0]\n",
    "    xx = np.linspace(X_train[0].min() - 1, X_train[0].max() + 1, 10)\n",
    "    yy = np.linspace(X_train[1].min() - 1, X_train[1].max() + 1, 10)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[0] / w[2] * X1 - w[1] / w[2] * X2 - b / w[2]\n",
    "    ax1.plot_surface(X1, X2, Z, alpha=0.5, rstride=1, cstride=1, color='k', linewidth=0)\n",
    "\n",
    "    # RG 图 21\n",
    "    ax2 = axs[1]\n",
    "    ax2.scatter(X_train[2], X_train[1], c=y_train, cmap=plt.cm.Paired)\n",
    "    ax2.set_xlabel('R')\n",
    "    ax2.set_ylabel('G')\n",
    "    ax2.set_title('RG Channels')\n",
    "\n",
    "    # 在RG图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 2], support_vectors[:, 1], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')\n",
    "\n",
    "    # 绘制RG的决策边界\n",
    "    xx = np.linspace(X_train[2].min() - 1, X_train[2].max() + 1, 500)\n",
    "    yy = np.linspace(X_train[1].min() - 1, X_train[1].max() + 1, 500)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[2] / w[1] * X1 - b / w[1]\n",
    "    ax2.contour(X1, X2, Z, colors='k', levels=[0], linestyles=['-'])\n",
    "\n",
    "    # RB 图 20\n",
    "    ax3 = axs[2]\n",
    "    ax3.scatter(X_train[2], X_train[0], c=y_train, cmap=plt.cm.Paired)\n",
    "    ax3.set_xlabel('R')\n",
    "    ax3.set_ylabel('B')\n",
    "    ax3.set_title('RB Channels')\n",
    "\n",
    "    # 在RB图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 2], support_vectors[:, 0], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')\n",
    "\n",
    "    # 绘制RB的决策边界\n",
    "    xx = np.linspace(X_train[2].min() - 1, X_train[2].max() + 1, 500)\n",
    "    yy = np.linspace(X_train[0].min() - 1, X_train[0].max() + 1, 500)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[2] / w[0] * X1 - b / w[0]\n",
    "    ax3.contour(X1, X2, Z, colors='k', levels=[0], linestyles=['-'])\n",
    "\n",
    "    # BG 图 01\n",
    "    ax4 = axs[3]\n",
    "    ax4.scatter(X_train[0], X_train[1], c=y_train, cmap=plt.cm.Paired)\n",
    "    ax4.set_xlabel('B')\n",
    "    ax4.set_ylabel('G')\n",
    "    ax4.set_title('BG Channels')\n",
    "\n",
    "    # 在BG图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 0], support_vectors[:, 1], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')    \n",
    "\n",
    "    # 绘制BG的决策边界\n",
    "    xx = np.linspace(X_train[0].min() - 1, X_train[0].max() + 1, 500)\n",
    "    yy = np.linspace(X_train[1].min() - 1, X_train[1].max() + 1, 500)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[0] / w[1] * X1 - b / w[1]\n",
    "    ax4.contour(X1, X2, Z, colors='k', levels=[0], linestyles=['-'])\n",
    "\n",
    "    # 自动调整子图布局，避免重叠\n",
    "    plt.tight_layout()\n",
    "    # 显示图形\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31ca4087-55e9-4956-bfd4-14e5617ee1a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n",
    "\n",
    "# 保存模型\n",
    "dump(clf, './model/svm_model_kernel.joblib')\n",
    "\n",
    "# 加载模型\n",
    "clf_loaded = load('./model/svm_model_kernel.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5815d8fd-2c09-4c61-8063-07ba9f8ec997",
   "metadata": {},
   "source": [
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e893c72-470d-467e-a51d-19b9d31151ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "from joblib import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a1bf4d3-9411-495c-8c14-b84805b95fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(model_path):\n",
    "    return load(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9658fa3c-fff7-4d4f-864f-ca7b95ff9c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image):\n",
    "    h, w, _ = image.shape\n",
    "    \n",
    "    blue_channel = image[:, :, 0].reshape(-1)\n",
    "    green_channel = image[:, :, 1].reshape(-1)\n",
    "    red_channel = image[:, :, 2].reshape(-1)\n",
    "\n",
    "    img_array = np.stack((blue_channel, green_channel, red_channel), axis=1)\n",
    "\n",
    "    print(img_array.shape)\n",
    "    \n",
    "    return img_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b4da2fb2-4cd8-41b4-92dd-8edd8a99df92",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = './model/svm_model_kernel.joblib'\n",
    "clf = load_model(model_path)\n",
    "\n",
    "# 加载测试集来源\n",
    "new_image = cv2.imread(new_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ad350af-aeea-4f98-9c7a-ec25d50bfb83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125000, 3)\n"
     ]
    }
   ],
   "source": [
    "new_features = preprocess_image(new_image)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51d0b2da-7d30-49ec-9278-b1542ad027d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((125000, 3), numpy.ndarray)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用相同的StandardScaler进行标准化\n",
    "new_features = scaler.transform(new_features)\n",
    "new_features.shape, type(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "43cde813-fa18-4def-8874-85d46abe880f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用模型进行预测\n",
    "prediction = clf.predict(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c21bf4da-84fd-4e35-8df7-3bed81d7c0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = clf.predict(new_features)\n",
    "\n",
    "# 将预测结果应用到图片上\n",
    "# 假设 original_image 是原始图片的NumPy数组\n",
    "original_image = new_image\n",
    "height, width, _ = original_image.shape\n",
    "predicted_image = np.zeros_like(original_image)\n",
    "\n",
    "for i in range(height):\n",
    "    for j in range(width):\n",
    "        # 获取当前像素的索引\n",
    "        pixel_index = i * width + j\n",
    "        \n",
    "        # 根据预测结果设置像素的颜色\n",
    "        if predictions[pixel_index] == 1:\n",
    "            predicted_image[i, j] = (255, 255, 255)  # 白色\n",
    "        else:\n",
    "            predicted_image[i, j] = (0, 0, 0)  # 黑色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12de4fb7-4df6-4c8b-b408-2273e821ba5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted image saved to ./result_new/pred_1066_multi_2.jpg\n"
     ]
    }
   ],
   "source": [
    "# 显示原始图片\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))\n",
    "plt.title('Original Image')\n",
    "plt.axis('off')\n",
    "\n",
    "# 显示预测后的图片\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(cv2.cvtColor(predicted_image, cv2.COLOR_BGR2RGB))\n",
    "plt.title('Predicted Image')\n",
    "plt.axis('off')\n",
    "\n",
    "# 显示图像\n",
    "plt.show()\n",
    "    \n",
    "# 保存预测后的图片\n",
    "new_image_name = os.path.splitext(os.path.basename(new_image_path))[0]\n",
    "save_path = f'./result_new/pred_{new_image_name}_multi_2.jpg'\n",
    "cv2.imwrite(save_path, predicted_image)\n",
    "print(f'Predicted image saved to {save_path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e90dc151-1d7a-4859-bcf0-cf0c92080018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 3.9541146755218506 seconds\n"
     ]
    }
   ],
   "source": [
    "end_time = time.time()\n",
    "# 计算并打印运行时间\n",
    "elapsed_time = end_time - start_time\n",
    "print(f\"Elapsed time: {elapsed_time} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8579604a-a5ae-42ca-98b0-abe97374ea7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel Accuracy (PA): 0.9463\n",
      "Intersection over Union (IoU): 0.7915\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def calculate_performance_metrics(y_true, y_pred):\n",
    "    # 计算混淆矩阵\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "    \n",
    "    # 计算像素准确率（PA）\n",
    "    pa = (tp + tn) / (tp + tn + fp + fn)\n",
    "    \n",
    "    # 计算交并比（IoU）\n",
    "    iou = tp / (tp + fp + fn)\n",
    "    \n",
    "    return pa, iou\n",
    "\n",
    "def load_and_mask_binary_image(image_path):\n",
    "    # 加载图像\n",
    "    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "    if image is None:\n",
    "        raise ValueError(f\"无法加载图像：{image_path}\")\n",
    "    \n",
    "    # 创建掩码以仅保留二值像素（0或255）\n",
    "    mask = (image == 0) | (image == 255)\n",
    "    # 应用掩码并转换为二值图像\n",
    "    binary_image = image.copy()\n",
    "    binary_image[~mask] = 0  # 将非二值像素设置为0\n",
    "    return binary_image, mask\n",
    "\n",
    "# 输入图像的路径\n",
    "test_image_path = save_path  # 测试得到的图像路径\n",
    "annotated_image_path = f'./input_data/{data_name}_label.png'  # 标注的图像路径\n",
    "\n",
    "# 加载并处理图像\n",
    "y_true, true_mask = load_and_mask_binary_image(annotated_image_path)\n",
    "y_pred, _ = load_and_mask_binary_image(test_image_path)\n",
    "\n",
    "# 将图像转换为二值数组（0或1）\n",
    "y_true_binary = (y_true > 0).astype(int)\n",
    "y_pred_binary = (y_pred > 0).astype(int)\n",
    "\n",
    "# 应用掩码以仅考虑二值像素\n",
    "y_true_binary = y_true_binary[true_mask]\n",
    "y_pred_binary = y_pred_binary[true_mask]\n",
    "\n",
    "# 计算性能指标\n",
    "pa, iou = calculate_performance_metrics(y_true_binary, y_pred_binary)\n",
    "\n",
    "print(f\"Pixel Accuracy (PA): {pa:.4f}\")\n",
    "print(f\"Intersection over Union (IoU): {iou:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "051b9da4-412e-41a2-aff8-710b3697220f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
